Completed
The target system: Online Boutique
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Automating Performance Tuning with Machine Learning
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 SREs care about efficiency and performan
- 3 Tuning system configuration matters...
- 4 but it is getting harder and harder
- 5 Key requirements for a new approach
- 6 ML techniques for smart exploration
- 7 ML enables automated performance tuning
- 8 and a new performance tuning process
- 9 The target system: Online Boutique
- 10 Use Case: optimizing cost of K8s microservices while ensuring reliability
- 11 The reference architecture
- 12 The optimization goals & constraints
- 13 Best configuration found by ML in 24H improves cost efficiency by 77%
- 14 Best config: optimal resources assigned to microservices
- 15 Best config: higher performance & efficiency for the overall service Baseline vs Best Service throughout Baseline vs Best Service po response time
- 16 Use Case: maximizing service performance & efficiency with JVM tuning
- 17 Best config: +28% throughput, and meeting SLOS
- 18 Best config: optimal JVM options 8
- 19 Key takeaways
